Relevant bibliographies by topics / Netlogo platform

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Netlogo platform'

Author: Grafiati
Published: 5 June 2025
Last updated: 24 June 2025

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Journal articles on the topic "Netlogo platform"

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Pigorev, Dmitry. "Mesa Python platform for agent-based modelling." Artificial societies 16, no. 3 (2021): 0. http://dx.doi.org/10.18254/s207751800008176-2.

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Mesa is a licensed by Apache development environment for agent-based models (or ABM) in Python. It allows users to create agent-based models using built-in basic components (such as spatial and agent schedulers) or custom implementations; visualize them using the interface in a web browser; and analyze their results using Python data analysis tools. The purpose of the development of the environment was the desire of the authors to create an alternative for NetLogo, Repast or MASON based on the Python language.
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Bezzout, Hamid, and Hanan El Faylali. "Agent-based model simulation for ground penetration radar based on Netlogo platform." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (2022): 576. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp576-586.

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Ground penetrating radar is used to detect magnetic materials underground by transmitting an electromagnetic wave into the material and receive the reflected pulse. In this paper, we develop a new agent-based model to simulate and evaluate the behavior of the ground penetrating radar based on Maxwell’s equations. This model contains several agents that represent the electric field, the magnetic field, the transmitted wave, the simulated medium, and the object to be detected. The implementation of this model is performed in Netlogo plaform because of its simplicity of coding and robustness of simulation. In order to validate our model, we have simulated the effect of the medium characterized by a dielectric constant and a conductance on the transmitted wave to evaluate the behavior of ground penetrating radar. The results obtained are compared with the literrature. Findings demonstrate that the transmitted pulse in the form of Gaussian pulse is reflected when it interacts with the object to detect. Thus, the ground penetrating radar can be efficiently simulated on Netlogo platform.
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Bezzout, Hamid, and Hanan El Faylali. "Agent-based model simulation for ground penetration radar based on Netlogo platform." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (2022): 576–86. https://doi.org/10.11591/ijeecs.v26.i1.pp576-586.

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Ground penetrating radar is used to detect magnetic materials underground by transmitting an electromagnetic wave into the material and receive the reflected pulse. In this paper, we develop a new agent-based model to simulate and evaluate the behavior of the ground penetrating radar based on Maxwell’s equations. This model contains several agents that represent the electric field, the magnetic field, the transmitted wave, the simulated medium, and the object to be detected. The implementation of this model is performed in Netlogo plaform because of its simplicity of coding and robustness of simulation. In order to validate our model, we have simulated the effect of the medium characterized by a dielectric constant and a conductance on the transmitted wave to evaluate the behavior of ground penetrating radar. The results obtained are compared with the literrature. Findings demonstrate that the transmitted pulse in the form of Gaussian pulse is reflected when it interacts with the object to detect. Thus, the ground penetrating radar can be efficiently simulated on Netlogo platform.
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Palrecha, Jiya, Prakash Hegade, and Ashok Shettar. "NetLogo Models for Pattern Recognition in Problem Based Learning." Journal of Engineering Education Transformations 38, IS2 (2025): 260–67. https://doi.org/10.16920/jeet/2025/v38is2/25031.

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Problem-based learning and computational thinking, when integrated as a teaching-learning pedagogy, can provide a platform for both teachers and students to explore how to teach and how to learn effectively. Pattern recognition, one of the key aspects of computational thinking, can assist computer science engineers in modeling problem scenarios through accommodation and assimilation. This study proposes a research question to analyze the effectiveness of pattern recognition in modeling the problem scenarios using Schema Theory as a conceptual framework. A multi-method approach was adopted as the research methodology, and self-selection was used as the sampling technique among students who enrolled in the model thinking course jointly offered by industry Knit Space and KLE Technological University. Technology plays a significant role in designing such problem scenarios. For this study, two problems were designed using NetLogo, and reflection points were provided for students to design models for each problem. NetLogo is a platform that offers various simulation models across multiple domains. Both the problems selected emphasized on the pattern recognition. The Paths and Wolf-Sheep models were used for the study. The study analyzed 50 student answer scripts using both qualitative and quantitative methods, after an informed consent to use the data for the research study. Through appropriate descriptive measures and statistical techniques, such as paired t-tests, student feedback, in-vivo coding, and process coding, the collected data was thoroughly examined to derive results and discussion points. Alongside statistical measures, the study also explored themes generated from the findings, with a focus on technology's role in the process. The results align positively with the conclusion that pattern recognition significantly aids in building better models.
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Qi, Hui, Mingyang Zhang, Hong Chen, and Feifei Liu. "Simulation of Chinese Coal Mine Safety Supervision System Performance Based on Netlogo Platform." Journal of Computational and Theoretical Nanoscience 13, no. 8 (2016): 5072–80. http://dx.doi.org/10.1166/jctn.2016.5384.

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Zhao, Chun Xiao, Xiu Fang Zhang, and Wei Na Gong. "Research on Simulation on Classroom Discipline Based on Complex System Theory with Scientific Teaching Materials." Applied Mechanics and Materials 63-64 (June 2011): 736–39. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.736.

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Classroom discipline is a complex and variable dynamic process, classroom problem behavior always leads classroom discipline problem that will affect teaching quality. The simulation on classroom discipline of multiagent system based on complex system theory uses dynamic simulation platform NetLogo. The result of simulation shows that this method can reproduce the dynamics of discipline behavior’s three stages in ecological classroom. This simulation provides forceful support for the classroom discipline management of teachers.
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ILYINSKY, A. I. "AGENT MODELING OF COMPETITION AND COEVOLUTION OF FINTECH AND REGTECH2 IN THE GLOBAL FINANCIAL ECOSYSTEM: THE RED QUEEN EFFECT." Scientific Works of the Free Economic Society of Russia 230, no. 4 (2021): 183–88. http://dx.doi.org/10.38197/2072-2060-2021-230-4-183-188.

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This paper examines the competition and co-evolution of FinTech and RegTech2 in the global financial ecosystem using the methods of evolutionary economics. A simple agent-based model is built on the NetLogo platform and the emergence of cycles of technological dominance in the ecosystem is shown. The paper also discusses the “technological arms race” of regulators and system participants as a result of the co-evolution and competition of two technology groups, known as the red queen effect.
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Ginovart, Marta, and Clara Prats. "A Bacterial Individual-Based Virtual Bioreactor to Test Handling Protocols in a Netlogo Platform." IFAC Proceedings Volumes 45, no. 2 (2012): 647–52. http://dx.doi.org/10.3182/20120215-3-at-3016.00115.

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Taherian, Mostafa, Seyed Mahmoud Mousavi, and Hooman Chamani. "An agent-based simulation with NetLogo platform to evaluate forward osmosis process (PRO Mode)." Chinese Journal of Chemical Engineering 26, no. 12 (2018): 2487–94. http://dx.doi.org/10.1016/j.cjche.2018.01.032.

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Zhang, Jiamin. "Simulation-Based Schedule Optimization for Virtual Coupling-Enabled Rail Transit Services with Multiagent Technique." Journal of Advanced Transportation 2023 (March 13, 2023): 1–21. http://dx.doi.org/10.1155/2023/3196066.

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Virtual coupling (VC) is a train-centric next generation signalling system, which can enable multiple trains to operate in a formation just like one train or decouple separately on-the-run or at station flexibly or as planned. With the aim of optimizing the interdeparture train headway time, providing the variable capacity for diverse passenger demand, maximizing the passenger riding comfort degree, and minimizing passenger travel cost and train operation cost, the dynamic schedule for VC-enabled rail transit services is investigated with the multiagent simulation technique on NetLogo platform. Our contribution is mainly fourfold. First, VC-enabled rail transit entity for simulation is represented with the multiagent technique, including representation of train unit, train convoy, passenger attributes and behavior, and mathematical formula for calculation of the train operation cost and passenger travel cost, as well as passengers riding comfort degree are proposed. Second, the operational principles for flexible and self-organisingVC-enabled trains are defined. Third, the VC-enabled train-centric, passenger demand-driven, and agent-based simulation flow and algorithms are developed innovatively, which adopt the ergodic strategy for simulation by traversing each O-D pair demand along each route section over the rail transit network. Finally, we test and discuss the proposed methodology on the designed computational experiment on the NetLogo platform, and the simulation results series validated the effectiveness of the proposed methodology. The provided research can effectively support the VC-enabled platoon operation-oriented train service schedule for future study.
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Dissertations / Theses on the topic "Netlogo platform"

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Railsback, Steven, Daniel Ayllón, Uta Berger, et al. "Improving Execution Speed of Models Implemented in NetLogo." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-221788.

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NetLogo has become a standard platform for agent-based simulation, yet there appears to be widespread belief that it is not suitable for large and complex models due to slow execution. Our experience does not support that belief. NetLogo programs often do run very slowly when written to minimize code length and maximize clarity, but relatively simple and easily tested changes can almost always produce major increases in execution speed. We recommend a five-step process for quantifying execution speed, identifying slow parts of code, and writing faster code. Avoiding or improving agent filtering statements can often produce dramatic speed improvements. For models with extensive initialization methods, reorganizing the setup procedure can reduce the initialization effort in simulation experiments. Programming the same behavior in a different way can sometimes provide order-of-magnitude speed increases. For models in which most agents do nothing on most time steps, discrete event simulation—facilitated by the time extension to NetLogo—can dramatically increase speed. NetLogo’s BehaviorSpace tool makes it very easy to conduct multiple-model-run experiments in parallel on either desktop or high performance cluster computers, so even quite slow models can be executed thousands of times. NetLogo also is supported by efficient analysis tools, such as BehaviorSearch and RNetLogo, that can reduce the number of model runs and the effort to set them up for (e.g.) parameterization and sensitivity analysis.
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Railsback, Steven, Daniel Ayllón, Uta Berger, et al. "Improving Execution Speed of Models Implemented in NetLogo." JASSS, 2016. https://tud.qucosa.de/id/qucosa%3A30227.

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NetLogo has become a standard platform for agent-based simulation, yet there appears to be widespread belief that it is not suitable for large and complex models due to slow execution. Our experience does not support that belief. NetLogo programs often do run very slowly when written to minimize code length and maximize clarity, but relatively simple and easily tested changes can almost always produce major increases in execution speed. We recommend a five-step process for quantifying execution speed, identifying slow parts of code, and writing faster code. Avoiding or improving agent filtering statements can often produce dramatic speed improvements. For models with extensive initialization methods, reorganizing the setup procedure can reduce the initialization effort in simulation experiments. Programming the same behavior in a different way can sometimes provide order-of-magnitude speed increases. For models in which most agents do nothing on most time steps, discrete event simulation—facilitated by the time extension to NetLogo—can dramatically increase speed. NetLogo’s BehaviorSpace tool makes it very easy to conduct multiple-model-run experiments in parallel on either desktop or high performance cluster computers, so even quite slow models can be executed thousands of times. NetLogo also is supported by efficient analysis tools, such as BehaviorSearch and RNetLogo, that can reduce the number of model runs and the effort to set them up for (e.g.) parameterization and sensitivity analysis.
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"A Bio-Inspired Algorithm and Foldable Robot Platform for Collective Excavation." Master's thesis, 2018. http://hdl.handle.net/2286/R.I.50513.

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abstract: Existing robotic excavation research has been primarily focused on lunar mining missions or simple traffic control in confined tunnels, however little work attempts to bring collective excavation into the realm of human infrastructure. This thesis explores a decentralized approach to excavation processes, where traffic laws are borrowed from swarms of fire ants (Solenopsis invicta) or termites (Coptotermes formosanus) to create decision rules for a swarm of robots working together and organizing effectively to create a desired final excavated pattern. First, a literature review of the behavioral rules of different types of insect colonies and the resulting structural patterns over the course of excavation was conducted. After identifying pertinent excavation laws, three different finite state machines were generated that relate to construction, search and rescue operations, and extraterrestrial exploration. After analyzing these finite state machines, it became apparent that they all shared a common controller. Then, agent-based NetLogo software was used to simulate a swarm of agents that run this controller, and a model for excavating behaviors and patterns was fit to the simulation data. This model predicts the tunnel shapes formed in the simulation as a function of the swarm size and a time delay, called the critical waiting period, in one of the state transitions. Thus, by controlling the individual agents' behavior, it was possible to control the structural outcomes of collective excavation in simulation. To create an experimental testbed that could be used to physically implement the controller, a small foldable robotic platform was developed, and it's capabilities were tested in granular media. In order to characterize the granular media, force experiments were conducted and parameters were measured for resistive forces during an excavation cycle. The final experiment verified the robot's ability to engage in excavation and deposition, and to determine whether or not to begin the critical waiting period. This testbed can be expanded with multiple robots to conduct small-scale experiments on collective excavation, such as further exploring the effects of the critical waiting period on the resulting excavation pattern. In addition, investigating other factors like tuning digging efficiency or deposition proximity could help to transition the proposed bio-inspired swarm excavation controllers to implementation in real-world applications.<br>Dissertation/Thesis<br>Masters Thesis Mechanical Engineering 2018
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Book chapters on the topic "Netlogo platform"

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Bezzout, Hamid, and Hanan El Faylali. "EmPRM: A Novel Multi-agent Model for Modeling and Simulating of Electromagnetic Waves Using Netlogo Platform." In Advances on Smart and Soft Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5559-3_43.

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Sakellariou, Ilias, Petros Kefalas, and Ioanna Stamatopoulou. "An Intelligent Agents and Multi-Agent Systems Course Involving NetLogo." In Multi-Agent Systems for Education and Interactive Entertainment. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-60960-080-8.ch002.

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In the context of an Agent and Multi-Agent Systems course, exposing students to problems and issues related to agent-based programming allows them to understand at a deeper level the otherwise theoretical aspects involved in the design of a multi-agent system (MAS). Indeed, educators have reported a variety of environments and techniques they use in order to increase active learning. The authors argue that NetLogo presents an excellent platform for the task, since it provides, among other interesting features, a small learning curve and an easy to setup visualization environment. This chapter records their experience in teaching a MAS course, using NetLogo as the vehicle for practical coursework. In addition, two extra NetLogo libraries that were provided to students are described, one for BDI-like agents (Belief-Desire-Intention, i.e. goal-oriented agents) and one for ACL-like (Agent Communication Language) communication, which were specifically developed for allowing students to implement more complex agent societies than the original NetLogo platform allowed.
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Yachou, Najlae, and Rajae Aboulaich. "Agent-Based Modeling and Simulation for Home Financing “Application in NetLogo Platform”." In Applications of Agent-Based Simulation in Islamic Finance. Islamic Development Bank Institute, 2024. http://dx.doi.org/10.55780/bk24012.

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Conference papers on the topic "Netlogo platform"

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Ruan, Guoxiang, and Pingnan Ruan. "Knowledge Sharing within Innovation Network Members with Netlogo Simulation Platform." In 2011 International Conference on Information Technology, Computer Engineering and Management Sciences (ICM). IEEE, 2011. http://dx.doi.org/10.1109/icm.2011.86.

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Kowalska-Pyzalska, Anna. "Willingess to pay for green energy: An agent-based model in NetLogo platform." In 2017 14th International Conference on the European Energy Market (EEM). IEEE, 2017. http://dx.doi.org/10.1109/eem.2017.7981943.

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Bazzanella, Eloísa, and Fernando Santos. "Does a Q-Learning NetLogo Extension Simplify the Development of Agent-based Simulations?" In Workshop-Escola de Sistemas de Agentes, seus Ambientes e Aplicações. Sociedade Brasileira de Computação, 2021. https://doi.org/10.5753/wesaac.2021.33403.

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Agent-based modeling and simulation is a simulation paradigm that allows focusing on individuals, their interactions, and the complex behavior that emerges from them. Agent-based simulations are typically developed in simulation platforms that provide features related to agents. One such platform is NetLogo, to which a reinforcement learning extension was made available recently. The extension provides commands for using the Q-Learning algorithm, but no evaluation on whether it simplifies the development of simulations is available. This paper presents a quantitative evaluation on using the extension in two simulations: the classic cliff walking problem; and a real-world, adaptive traffic signal control (ATSC) simulation. Results show that the size of simulations source code developed using the extension is smaller than those developed without using it, giving evidence that the extension simplifies the development of simulations
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Barbosa, Raquel de Miranda, and Antônio Carlos da Rocha Costa. "Extending deontic interpreted systems with action logic." In Workshop-Escola de Sistemas de Agentes, seus Ambientes e Aplicações. Sociedade Brasileira de Computação, 2013. https://doi.org/10.5753/wesaac.2013.33165.

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This paper presents an extension to deontic interpreted systems with the use of action logic for the specification of normative aspects in multiagent systems. The paper presents a preliminary formalization of operators required and describes a simple example of the application of this formalization, using the segregation model (a simulation model available in NetLogo platform) through the formalization and proof of some properties of this system.
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Bourgeous, William, Shelley Rounds, and YangQuan Chen. "A Swarm Engineering Approach to Mobile Sensor Network Design Towards Collaborative Phototaxis With A Slowly Moving Light Source." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34320.

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Swarm robotics is an innovative approach to the control and coordination of multi-agent systems that use naturally inspired swarm intelligent methods to perform tasks. A swarm based approach can decrease the complexity and the cost of designing a cooperative multi-robot system. This paper proposes a general engineering approach to develop a robotic swarm that focuses on how to synthesize an emergent behavior and the associated inputs to this end. We validate our methodology by engineering a swarm to simultaneously rendezvous on a stationary light source. Furthermore, we also considered the case when the light source is slowly moving. The design is simulated in NetLogo, an agent-based modeling software, and implemented on the MASnet robot platform. This work demonstrates the basic knowledge and tools required to engineer a robotic swarm.
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